An assessment of estimation methods for generalized linear mixed models with binary outcomes.
An assessment of estimation methods for generalized linear mixed models with binary outcomes.
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DOI：
10.1002/sim.5866
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发表时间：
2013-11-20
影响因子：
2
通讯作者：
Begg, Colin B.
中科院分区：
文献类型：
--
作者：
Capanu, Marinela;Goenen, Mithat;Begg, Colin B.
Two main classes of methodology have been developed for addressing the analytical intractability of generalized linear mixed models (GLMMs): likelihood-based methods and Bayesian methods. Likelihood-based methods such as the penalized quasi-likelihood approach have been shown to produce biased estimates especially for binary clustered data with small clusters sizes. More recent methods using adaptive Gaussian quadrature perform well but can be overwhelmed by problems with large numbers of random effects, and efficient algorithms to better handle these situations have not yet been integrated in standard statistical packages. Bayesian methods, though they have good frequentist properties when the model is correct, are known to be computationally intensive and also require specialized code, limiting their use in practice. In this article we introduce a modification of the hybrid approach of Capanu and Begg as a bridge between the likelihood-based and Bayesian approaches by employing Bayesian estimation for the variance components followed by Laplacian estimation for the regression coefficients. We investigate its performance as well as that of several likelihood-based methods in the setting of GLMMs with binary outcomes. We apply the methods to three datasets and conduct simulations to illustrate their properties. Simulation results indicate that for moderate to large numbers of observations per random effect, adaptive Gaussian quadrature and the Laplacian approximation are very accurate, with adaptive Gaussian quadrature preferable as the number of observations per random effect increases. The hybrid approach is overall similar to the Laplace method, and it can be superior for data with very sparse random effects.
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影响因子：
1.9
作者：
Capanu M;Begg CB
通讯作者：
Begg CB
影响因子：
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作者：
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通讯作者：
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DOI：
10.2307/2983404
发表时间：
1995-01-01
影响因子：
2
作者：
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通讯作者：
GOLDMAN, N
DOI：
10.1080/03610910802627055
发表时间：
2009-01-01
影响因子：
0.9
作者：
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通讯作者：
Lim, Johan
影响因子：
2.4
作者：
Raudenbush, SW;Yang, ML;Yosef, M
通讯作者：
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